The List of Conference
Papersthe name of the presenting author is
underlined

Paper ID

General Track: Full Papers

F26

Modeling Single-Gyroid
Structures in Surface Mesh Models for 3D PrintingJidong Wang, Ruibin Zhao and Mingyong Pang
How to improve strength-to-weight ratio of printed models is an important
topic in 3D printing. We in this paper propose a novel structure modeling
method based on the implicit function technique and the finite element method
(FEM). Our method first obtains a set of sampled points in a given surface
mesh model by using a probability-based strategy, and generates an adaptive
tetrahedral mesh from the points. FEM is then used to analyze the stress of
the tetrahedral mesh and a stress map of the input model is created. The
method finally builds a result model composed of a shell and an interior
single-gyroid lattice. The lattice is defined by a piecewise 3D implicit
function, and has several special structural properties just like the
structure of the light but strong butterfly wing. The lattice together with
the shell forms a natural 3D structure for 3D printing. Local thickness of
lattice rods in the structure adaptively changes with stress distribution for
withstanding external loads. Experimental results show that our method can
deal with various surface mesh models in rapid way, and the resulted models
for 3D printing have high strength-to-weight ratios.

F35

Color Preference
Differences between Head Mounted Displays and PC ScreensAndreas Siess, Matthias Wölfel and Nico Häffner
Recently virtual reality (VR) applications are shifting from professional
use cases to more entertainment-centered approaches. Therefore aesthetic
aspects in virtual environments gain in relevance. This paper examines the
influence of different color determining parameters on user perception habits
between head mounted displays (HMD) and computer screens. We conducted an
empirical study with 50 persons that were asked to adjust the color
temperature, saturation and contrast according to their personal preferences
using a HMD as well as a computer screen, respectively. For cross validation
we tested a second user group of 36 persons that were asked to adjust the
color temperature exclusively. By using a set of five different panorama
images—each of them representing an exemplary scenario—we have
found that color perception differs significantly. This depends on the used
output device as well as gender: i.e. females preferred a significantly
colder color scheme in VR compared to their preferences on the computer
screen. Furthermore they also chose a significant colder color scheme on the
HMD compared to their male counterparts. Our findings demonstrate that
content created for conventional screens can not simply be transferred to
immersive virtual environments but for optimal results needs reevaluation of
its visual aesthetics.

F40

Facial Expression Editing
in Face Sketch using Shape Space TheoryChenlei Lv, Zhongke Wu, Xingce
Wang, Dan Zhang, Xiangyuan Liu and Mingquan Zhou
Facial expression editing in face sketch is an important and challenging
problem in computer vision community as facial animation and modeling. For
criminal investigation and portrait drawing, automatic expression editing
tools for face sketch improve work efficiency obviously and reduce
professional requirements for users. In this paper, we propose a novel method
for facial expression editing in face sketch using shape space theory. The
new facial expressions in the sketch images can be regenerated automatically.
The method includes two components: 1) face sketch modeling; 2) expression
editing. The face sketch modeling constructs 3D face sketch data from 3D
facial database to match the 2D face sketch. Using facial landmarks, the
“shape” of the face sketch is represented in shape space. The shape space is
a manifold space which removes the rigid transform group. In shape space, the
accurate 3D face sketch model is obtained which is consistent to the original
2D face sketch. For expression editing, we change the parameters of 3D face
sketch model in the shape space to obtain new expressions. The expression
transfer in 3D face sketch model can be mapped into the 2D face sketch. The
advantages of our method are: full-automatic in modeling process; no
requirements of drawing skills to user and friendly interaction; robustness
to head poses and different scales. In experiments, we use the 3D facial
database, FaceWareHouse, to construct the 3D face sketch model and use face
sketch images from database: CUHK Face sketch Database (CUFS) to show the
performance of expression editing. Experimental results demonstrate that our
method can effectively edit facial expressions in face sketch with high
consistency and fidelity.

F50

A Robust and Efficient
Algorithm for Multi-body Continuous Collision DetectionBinbin Qi and Mingyong Pang
Multi-body collision detection is a key and important technology in
societies of computer graphics, system simulation, virtual reality, etc, and
has been widely used in various applications. To deal with the collision
problems in large scale multi-body simulations robustly and efficiently, we
in this paper proposed a robust and efficient algorithm of continuous
multi-body collision detection based on the kinetic “sweep and prune” (SaP)
technique and the event-driven mechanism. Our algorithm first culls redundant
detection calculations among very large numbers of moving bodies, and then
automatically generates events to predict these collisions, probably taken
place in coming time, of the object pairs. All these events are been pushed
into a priority queue, which is used to drive our algorithm to run. By
introducing a new hybrid bounding box hierarchy in the event processing
process, our algorithm can detect positions where the object pairs collide.
We discovered the event blocking problem potentially occurred during event
processing, and further proposed several methods to alarm or relieve the
system from the event blocking state. Experimental results show that our
algorithm has good stability and strong robustness, and it can improve the
speed and accuracy of the multi-body collision detection effectively.

F60

Enhancing Sketching and
Sculpting for Shape Modeling
Kai Wang, Jianmin Zheng and Hock Soon Seah
Sketch-based modeling uses freeform strokes as basic modeling metaphor and
provides an intuitive way for shape modeling, for instance, for cyberworlds.
This paper presents a new method to enhance sketch-based modeling. The core
idea of the method is to enhance the sketching process by allowing the user
to iteratively sketch to progressively create initial shapes that interpolate
the sketched strokes. This process considers all the sketches and the
up-to-date constructed 3D shape, which enables the user to be aware of the
shape of the sketched model. The key underlying technique that supports this
process is a novel surface construction algorithm, which generates 3D
triangular mesh models with gradual shape changes during iterative sketching.
Experiments demonstrate that the presented method can allow users to
intuitively and flexibly create and edit 3D models even with complex
topology, which is usually difficult in existing sketch-based modeling
systems.

F64

An Experimental Comparison
of Text Classification TechniquesSuyash Lakhotia and Xavier Bresson
Text classification is the task of labeling text data from a predetermined
set of thematic labels. It has become of increasing importance in recent
years as we generate large volumes of data and require the ability to search
through these vast datasets with flexible queries. However, manually labeling
text data is an extremely tedious task that is prone to human error. Thus,
text classification has become a key focus of machine learning research, with
the goal of producing models that are more efficient and accurate than
traditional methods. The objective of this work is to rigorously compare the
performance of current text classification techniques, from standard
SVM-based, statistical and multilayer perceptron (MLP) models to recently
enhanced deep learning models such as convolutional neural networks and their
fusion with graph theory. Extensive numerical experiments on three major text
classification datasets (Rotten Tomatoes Sentence Polarity, 20 Newsgroups and
Reuters Corpus Volume 1) revealed two results. First, graph convolutional
neural networks perform with greater or similar test accuracy when compared
to standard convolutional neural networks, SVM-based models and statistical
baseline models. Second, and more surprisingly, simpler MLP models still
outperform recent deep learning techniques despite having fewer parameters.
This implies that either benchmark datasets like RCV1 containing more than
420,000 documents from 52 classes are not large enough or the representation
of text data as tf-idf document vectors is not expressive enough.

F69

MaeSTrO: A Mobile App for
Style Transfer Orchestration using Neural Networks
Max Reimann, Mandy Klingbeil, Sebastian Pasewaldt, Amir Semmo, Matthias Trapp and
Jürgen Döllner
Mobile expressive rendering gained increasing popularity among users seeking
casual creativity by image stylization and supports the development of mobile
artists as a new user group. In particular, neural style transfer has
advanced as a core technology to emulate characteristics of manifold artistic
styles. However, when it comes to creative expression, the technology still
faces inherent limitations in providing low-level controls for localized
image stylization. This work enhances state-of-the-art neural style transfer
techniques by a generalized user interface with interactive tools to
facilitate a creative and localized editing process. Thereby, we first
propose a problem characterization representing trade-offs between visual
quality, run-time performance, and user control. We then present MaeSTrO, a
mobile app for orchestration of neural style transfer techniques using
iterative, multi-style generative and adaptive neural networks that can be
locally controlled by on-screen painting metaphors. At this, first user tests
indicate different levels of satisfaction for the implemented techniques and
interaction design.

F76

What User Interface to Use
for Virtual Reality? 2D, 3D or Speech–A User Study
Yannick Weiß, Daniel
Hepperle, Andreas Sieß and Matthias Wölfel
In virtual reality different demands on the user interface have to be
addressed than on classic screen applications. That’s why established
strategies from other digital media cannot be transferred unreflected and at
least adaptation is required. So one of the leading questions is: which form
of interface is preferable for virtual reality? Are 2D interfaces—that
are mostly used in combination with mouse or touch interactions— the
means of choice, although they do not use the medium’s full capabilities?
What about 3D interfaces that can be naturally integrated into the virtual
space? And last but not least: are speech interfaces, the fastest and most
natural form of human interaction/communication, which have recently
established themselves in other areas (e.g., digital assistants), ready to
conquer the world of virtual reality? To answer these question this work
compares these three approaches based on a quantitative user study and
highlights advantages and disadvantages of the respective interfaces for
virtual reality applications.

F83

An Automatic Method for
Semantic Focal Feature Point Tracking of 3D Human Model in Motion SequencePeng Xiaoyu, Tan Xiaohui and Wang Kang
In this paper, a method, for automatically identifying and tracking garment
related semantic focal feature points on 3D human model in motion sequence,
is proposed. We consider the problem of automatic focal feature point
identification and tracking when non-rigid shape deformation is occurred. The
main contribution is that a novel method of tracking focal feature points
when the human avatar move in front of depth camera. Firstly, we learn a
regression analysis model that derives the relationship between sampled and
focal feature points. Secondly, we build a model of correspondence maps to
calculate the tracking results. The method can track garment-related feature
points for different people in different motion and shape. We demonstrate on
a wide variety of experiments that our approach leads to a significant
identification and tracking result with input depth sequences.

F88

Self-Training System for
Tennis Shots with Motion Feature Assessment and VisualizationMasaki Oshita, Takumi Inao, Tomohiko Mukai and Shigeru Kuriyama
This paper describes a prototype self-training system for tennis forehand
shots that allows trainees to practice their motion forms by themselves. Our
system uses a motion capture device to record a trainee’s motion, and
visualizes the differences between the features of the trainee’s motion and
the correct motion as performed by an expert. This system enables trainees to
understand the errors in their motion and how to reduce or eliminate them. In
this study, we classified the motion features and corresponding visualization
methods using one- dimensional spatial, rotational, and temporal features
based on the key sporting poses. We also developed a statistical model for
the motion features, allowing the system to assess and prioritize all features
of a trainee’s motion. This research focuses on the motion of a tennis
forehand shot and evaluates our prototype through several user experiments.

F101

LifeBrush: Painting
Interactive Agent-based SimulationsTimothy Davison, Faramarz Samavati
and Christian Jacob
Building and interacting with 3D agent-based simulations that contain a
large number of agents is a significant challenge. What if we want to create
an intricate new arrangement of agents, or reconfigure a large number of
agents? We present LifeBrush, a cyberworld for interactively painting large
and elaborate multi-agent simulations with commodity virtual reality systems
that we can then simulate and explore. Our main methodology uses sketch-based
discrete element texture synthesis to paint agent arrangments. We define a
map to convert agents to elements in this framework when we paint and back to
agents when we simulate. Like creating new colors on a paint palette, we
create example agent arrangements and configurations in an example palette. We
paint new agents into a scene with sketch-based generative brushes. We also
use those brushes to reconfigure agents to match examples created in the
palette. Then we simulate, pause the simulation and modify the agents with
our sketch-based tools. This iteration loop enables new levels of
interactivity for the design, simulation, and exploration of agent-based
simulations.

General Track: Short Papers

S8

Autonomous Virtual Player
in a Video Game Imitating Human Players: the ORION Framework
Cédric Buche, Cindy Even and Julien Soler
This paper introduces the design of autonomous virtual player based on
imitation learning using human behavior observations. The ORION model
provides both data mining techniques allowing the extraction of knowledge and
behavior models allowing the control of the autonomous behaviors. ORION is
also an operational tool allowing the representation, transformation,
visualization and prediction of data. We illustrate the use of our model by
detailing the implementation of a virtual player for the video game Unreal
Tournament 3. Thanks to ORION, data from low level behaviors were collected
through three scenarios performed by human players: movement, long range
aiming and close combat. Behaviors can then be learned from the obtained
data-sets after transformations and application of data mining techniques.
ORION allows us to build a complete behavior using an extension of a Behavior
Tree integrating ad hoc features in order to manage aspects of behavior that
we have not been able to learn automatically.

S10

Training an FCN with
Synthetic Images for Component Segmentation with Applications in Orientation
Estimation and Image Inpainting. Achim Rehberger, Kai Weber and Yvonne Jung
The detection and segmentation of real objects along with their components
is still an advanced topic. More detailed segmentation is needed to solve
tasks like position and orientation estimation or for doing inpainting of
components. In this paper, we specifically focus on cars and present a
segmentation of components, such as rims or lights, which requires detailed
and accurate training data. A decomposed 3D model is used to render highly
detailed images of the car that fit to corresponding ground truth images. The
main challenge is to create high quality synthetic datasets that allow
reliable and accurate segmentation of real-world footage. Different camera
shots, filters, environment maps, shapes, and neural networks are used and
their benefits as well as problems are discussed within this paper. The
accuracy and reliability of the segmentation depends on the quality and
variability of the rendered images. We started with simple 3D models and a
real-time renderer and reached accurate and reliable segmentation with almost
photorealistic images that are created with a global illumination renderer.
These results are then used for replacing components of the car as well as
for deriving the position of special points of interest, like the center of a
wheel, which is also necessary for subsequent processing such as correctly
aligning the 3D model with the real camera stream for Augmented Reality
applications. Here, the quality of replacing a component of an object with
its rendered 3D counterpart depends on the accuracy of segmentation.
Therefore, segmented components are used to determine the position and
orientation of the car along with the size of the inpainting area. Then, a
matching rendered image of the component is inpainted only into the segmented
area. In this regard, we also compare two different approaches for deriving
the center points of the components of an object.

S15

Glossy Reflections for
Mixed Reality Environments on Mobile DevicesTobias Schwandt, Christian Kunert
and Wolfgang Broll
Glossy reflections of the surroundings play a major role when trying to
achieve a seamless fusion of real and virtual objects in Mixed Reality (MR)
environments. Traditionally, the necessary information about the ambiance is
captured using mirrored balls, HDR cameras, fish-eye lenses, RGB-D cameras or
360-degree cameras. While these approaches allow for pretty good results,
they require a rather complex setup. Our approach is based on a single RGB
camera capturing the environmental lighting at a certain location within the
scene. Therefore, we apply a precomputation step generating a 360-degree
environment map and combine it with a camera-based image stitching for a
continuous enhancement and update of the lighting information. We show that
our approach allows for realistic and high-quality reflections within an
AR/MR environment in real time even on mobile devices.

S24

Text to 3D Model of
Chinese Ancient ArchitectureYan Wang, Pu Ren, Mingquan Zhou,
Wuyang Shui and Pengbo Zhou
Three-dimensional (3D) modeling is currently a creative task that requires
modelers with strong professional skills and background knowledge, especially
in the field of 3D modeling of Chinese ancient architecture (CAA). At
present, most of the studies on 3D CAA modeling are based on hard-coded
constructive rules, which need completed, complex and formalized
descriptions. We present a generative system bridging the gap between the
Chinese text and 3D models that allows users to generate 3D models by natural
language. First, a Bayesian network is learned from existing CAA data to
provide relationships of different structural components. Second, by parsing
the Chinese text inputted by the user, key components of the CAA will be
determined; and other matched structural components will be calculated by
inferencing the trained Bayesian network. Third, the synthesis of all
components is achieved by a proposed placement optimizing algorithm. Finally,
we evaluate the effectiveness of the trained Bayesian network and demonstrate
the application to generate 3D CAA model rapidly from the Chinese text.

S30

Reproducing Implicit
Curves with Sharp FeaturesJingjie Zhao, Jidong Wang, Ruibin
Zhao and Mingyong Pang
Implicit curves play an essential role in the societies of medicine,
meteorology, geology, geo-physics, visualization and so on. In this paper, we
propose an algorithm to visualize implicit curves and reproduce their sharp
features in 2D plane. To access the subdivision cells of a user-defined 2D
domain, our algorithm first creates a quadtree by using a top-down and
adaptive quad-tree construction technique. In each cell, the method locates
exact one feature point of the numerical field defined by the implicit
function defining an implicit curve. A discrete optimization technique is
employed to calculate the feature points. A dual mesh is subsequently
constructed for the quadtree by taking the feature points as its vertices.
Our algorithm approximates local part of the implicit curve in each cell of
the dual mesh with a modified version of the marching squares method.
Collecting all the approximations in the cells, our method finally reproduces
the implicit curve with sharp features. Experiments show that our method can
efficiently extract the sharp features of implicit curves, and it can work
with various implicit curves with or without sharp features robustly.

S31

On Multiple-view Matrix
Based 3D Reconstruction from Multiple-view ImagesHui-Min Huang, Rui-Bin Zhao and
Ming-Yong Pang
In this paper, we propose a multiple-view matrix based 3D reconstruction
algorithm for generating a 3D point cloud model for a scene or an object from
several sequence images. The algorithm first extracts a group of Scale
Invariant Feature Transform (SIFT) feature points from each image, and
divides the points into different groups according to the matching degrees
among the points. Secondly, a set of 3D point clouds are reconstructed from
the feature points with a calculated a multiple-view matrix. Then, a complete
result is generated by merging the point clouds with an incremental algorithm
and the estimated camera parameters. Furthermore, our result is optimized by
employing a Bundle Adjustment (BA) method. Owing to the introduction of the
multiple-view matrix and the group-based SIFT matching, our algorithm has the
ability to accurately reconstruct a 3D point cloud model only with several
images. The performance of our algorithm is evaluated on a group of benchmark
datasets, and is compared to two state-of-the-art methods.

S34

A Benchmark for Distance
MeasurementsUlrich
Krispel, Dieter W. Fellner and Torsten Ullrich
The need to analyze and visualize distances between objects arises in many
use cases. Although the problem to calculate the distance between two
polygonal objects may sound simple, real-world scenarios with large models
will always be challenging, but optimization techniques – such as space
partitioning – can reduce the complexity of the average case
significantly.
Our contribution to this problem is a publicly available benchmark to
compare distance calculation algorithms. Furthermore, we evaluated the two
most important techniques (hierarchical tree structures versus grid-based
approaches).

S45

Computer-aided Sugoroku
Games in the Edo Period Using Interactive Techniques for Museum ExhibitsAsako Soga, Masahito Shiba and Takuzi Suzuki
The purpose of this study is to raise interest in a kind of Japanese board
game Sugoroku in the Edo period, and to support exhibits of it at museums. We
developed a computer-aided Sugoroku games using modern interactive
techniques. In this system, the user rolls a dice-type device equipped with a
microcomputer. Since the system detects the values of the dice-type device,
the players can simply play by just throwing the die. By projecting the
game’s progress on the Sugoroku sheet with a ceiling projector, the system
shows the current positions of the players and the candidate destinations.
With this guide, they can play Sugoroku games even without knowing the rules.
The system was used at a special exhibition of the National Museum of
Japanese History for eight weeks. We evaluated our computer-aided Sugoroku
games with visitors on three days. Almost half of the visitors marked the
best score for all items, indicating that this system was successfully
accepted by them.

S47

Towards Asynchronous
Video-haptic Interaction in CyberspaceGuo Song and Alexei Sourin
Video-conferencing and video calls are common nowadays. However, without
being able to physically touch or feel each other, we cannot have full
immersive communication achieved. Unlike earlier attempts of joining video
and haptic communication into one integrated system, we propose to setup an
asynchronous method of exchanging haptic interaction data while using
traditional ways of video communication. The data packets are exchanged over
the Internet cloud server where only participating haptic interface point
coordinates, orientation angles of the device handles are transmitted. We
also explore more options like using depth-sensing cameras and hand-tracking
devices to capture motion of hand, arm or the whole body of one user so that
he becomes both visible and tangible to the other party. The proposed way of
communication is validated by technical measurements and a user study. A test
of remote connections over long distances was carried out to pave way for
further studies.

S51

A Framework for 3D Object
Segmentation and Retrieval using Local Geometric Surface Features
Dimitrios Dimou and Konstantinos
MoustakasRobotic
vision and in particular 3D understanding has attracted intense research
efforts the last few years due to its wide range of applications, such as
robot-human interaction, augmented and virtual reality etc, and the
introduction of low-cost 3D sensing devices. In this paper we explore one of
the most popular problems encountered in 3D perception applications, namely
the segmentation of a 3D scene and the retrieval of similar objects from a
model database. We use a geometric approach for both the segmentation and the
retrieval modules that enables us to develop a fast, low-memory footprint
system without the use of large-scale annotated datasets. The system is based
on the fast computation of surface normals and the encoding power of local
geometric features. Our experiments demonstrate that such a complete 3D
understanding framework is possible and advantages over other approaches as
well as weaknesses are discussed.

S55

A Figurative and
Non-topological Approach to Mathematical VisualizationAtsushi Miyazawa, Masanori Nakayama and Issei Fujishiro
The term figurative refers to any form of mathematical visualization that
retains strong references to the geometry found in the real world. This paper
explains the figuration process for some basic mathematical functions
definable in the n-dimensional complex projective space. In the latter part
of this paper, we raise a question that has been neglected thus far: What
does the Riemann sphere’s axis stand for? We show that the answer can be
obtained only by observing from the inside the sphere by setting the
viewpoint of the immersive environment to the origin, which is always
undefined in projective geometry. We also draw some basic math functions that
are familiar to us on the projective plane and observe the invariant
properties that exist among the functions, which were thought to be different
from one another.

S67

Bot Believability
Assessment: a Novel Protocol & Analysis of Judge Expertise
Cindy Even, Anne-Gwenn
Bosser and Cédric Buche
For video game designers, being able to provide both interesting and
human-like opponents is a definite benefit to the game’s entertainment value.
The development of such believable virtual players also known as Non-Player
Characters or bots remains a challenge which has kept the research community
busy for many years. However, evaluation methods vary widely which can make
systems difficult to compare. The BotPrize competition has provided some
highly regarded assessment methods for comparing bots’ believability in a
first person shooter game. It involves humans judging virtual agents
competing for the most believable bot title. In this paper, we describe a
system allowing us to partly automate such a competition, a novel evaluation
protocol based on an early version of the BotPrize, and an analysis of the
data we collected regarding human judges during a national event. We observed
that the best judges were those who play video games the most often,
especially games involving combat, and are used to playing against virtual
players, strangers and physically present players. This result is a starting
point for the design of a new generic and rigorous protocol for the
evaluation of bots’ believability in first person shooter games.

S74

Effects of Electrical Pain
Stimuli on Immersion in Virtual RealityMatthias Wölfel and Joey Schubert
The ultimate goal of virtual realty is to create a simulated world around us
which is indistinguishable from the physical world as we know it. In such an
environment our actions could lead to severe effects on our body. What would
happen if one gets hit by a bullet, car or lightning? How would the felt pain
change our perception of the virtual environment? It turns out that the
influence of nociception (pain) on the human perception in virtual
environments is not well covered in the scientific literature besides pain
control/management. The goal of this publication is to investigate the
influence of pain stimuli on immersion as well as decision making and to
foster research and discussion in this direction.

S77

Real-Time Art-Directed
Charcoal Cyber ArtsYee Xin Chiew, Hock Soon Seah and
Santiago E. Montesdeoca
In this paper, we present a stylization pipeline in 3D object space to
emulate traditional charcoal drawing style in real time for cyber arts.
First, we introduce an algorithm to produce a rough, grainy charcoal effect
based on the lighting available in a 3D scene and the height map of a paper
substrate. Then, to further refine the stylized result, we introduce several
methods to reproduce some common techniques used in charcoal drawings, such
as mixing, smudging and edge softening. The effects can be art-directed in
real time to achieve sophisticated charcoal renders that reflect the
aesthetic vision of the artist.

S86

GPS Trail Visualizer for
Online Communities
Andreas Chrisna Mayong, Vajisha U. Wanniarachchi, Owen Noel Newton Fernando and May
Oo Lwin
Due to the rising health issues and changes in people's daily routines,
people became more health conscious over the past few decades. As a result,
along with the rapid growth in mobile phones and emerging activity tracking
devices, lots of mobile applications were introduced to track and encourage
people's daily exercise routines. Among these applications, Global Position
System (GPS) technology-based applications facilitate users to visualize
their jogging/running trail in a 2-dimensional view on a google map and share
it with friends. This paper suggests a methodology to enhance the
visualization from 2-dimensional to 3-dimensional and more realistic visual
interpretation by developing a Hyperlapse video using Google Street View
Images.

S97

Parallel 3D Skeleton
Extraction using Mesh Segmentation
Iason Manolas, Aris Lalos and Konstantinos MoustakasThere are several works performing accurate skeleton
extraction, however, their main drawback is the extensive computational
requirements and the lack of solutions that can be executed in multi-core
computing systems. These challenges, become more demanding when we are
dealing with dense 3D models. To cope with this scarcity we propose a novel
method that extends a well known contraction-based skeletonization method,
enabling its decentralization resulting in significant improvement in
skeleton extraction times.

Track on Cognitive Human-machine Interaction:
Full Papers

F16

Force-Based Evolutionary
Computation Approach for Automatic Skeletal Motion Learning in Human
Animation
Francisco Calatayud, Luis de la Vega-Hazas and Andrés IglesiasThe realistic animation of human characters is a hot topic of
research in computer graphics, with remarkable applications in computer
animation and video games. A current trend is the application of powerful
evolutionary computation techniques to meet the needs of increasing
sophistication in the field. These techniques are inspired by the principles
and mechanisms of biological evolution, such as selection, mutation,
recombination, crossover, and so on. A great advantage of such methods is
that they do not make any assumption on the problem to be solved. In this
paper we present a new evolutionary computation approach for automatic
skeletal motion learning in human animation. This approach is intended to
generate automatically a sequence of motions on a human skeleton leading to
plausible and realistic movements of the human body. This sequence is
obtained autonomously (i.e. without human intervention) through an iterative
intelligent process where the digital characters learn the optimal values of
forces applied to selected bones according to the desired motion routines for
proper movement. An illustrative example is discussed in detail to show the
performance of this approach. This method can readily be adapted/extended to
other skeleton configurations and interesting motions with only minor
modifications.

F19

Stable Feature Selection
for EEG-based Emotion RecognitionZirui Lan, Olga Sourina, Lipo
Wang, Yisi Liu, Reinhold Scherer and Gernot R. Müller-Putz
Affective brain-computer interface (aBCI) introduces personal affective
factors into human-computer interactions, which could potentially enrich the
user’s experience during the interaction with a computer. However, affective
neural patterns are volatile even within the same subject. To maintain
satisfactory emotion recognition accuracy, the state-of-the-art aBCIs mainly
tailor the classifier to the subject-of-interest and require frequent
re-calibrations for the classifier. In this paper, we demonstrate that the
recognition accuracy of aBCIs deteriorates when re-calibration is ruled out
during the long-term usage for the same subject. Then, we propose a stable
feature selection method to choose the most stable affective features, for
mitigating the accuracy deterioration to a lesser extent and maximizing the
aBCI performance in the long run. We validate our method on a dataset
comprising six subjects’ EEG data collected during two sessions per day for
each subject for eight consecutive days.

F20

EEG-based Cadets Training
and Performance Assessment System in Maritime Virtual SimulatorYisi Liu, Zirui Lan, Olga Sourina, Hui Ping Liew, Gopala Krishnan,
Dimitrios Konovessis and Hock Eng Ang
Deep investment in the maritime industries has led to many cutting edge
technological advances in shipping navigation and operational safety to
ensure safe and efficient logistical transportations. However, even with the
best technology equipped onboard, maritime accidents are still occurring with
at least three quarters of them attributed to human errors. Due to the rising
need to address the human factors in shipping operations, various human
factors studies are conducted in maritime domain. In this paper, an
Electroencephalogram (EEG)-based cadets training and performance assessment
system is proposed and implemented that could be used in the maritime virtual
simulator. The system includes an EEG processing and analyses part and an
evaluation part. It could recognize the brain states such as mental workload,
emotions, and stress from raw EEG signal recorded during the exercises in the
simulator and then give an indicative recommendation on “pass”, “retrain”, or
“fail” of the cadet based on the EEG recognition results and input of the
level of the task difficulty performed.

F33

Classifying Brain
Activities in Perception of Shape-analogous English Letters Based on EEG
Signal
Rohit Bose, Sim Kuan Goh, Kian F Wong, Nitish Thakor, Anastasios Bezerianos and Junhua
Li
Brain computer interface (BCI) technique has been demonstrated that human
intentions or stimulus perception can be recognized using EEG signal recorded
from the human scalp. When an intention is initiated in the brain or an
external stimulus is perceived, the underlying relevant processing alters
brain activity. This alteration in brain activity can be reflected in EEG
signal. The intention or stimulus perception is therefore classified based on
the alteration in brain activity. It might be difficult to classify brain
activities in the perception of shape-analogous English letters because the
similar shape could lead to less difference in brain activity. In order to
explore classification feasibility and classification performance of
shape-analogous letters using EEG signal, we performed an experiment of
shape-analogous letter perception, in where participants perceived four
letters (i.e., ‘p’, ‘q’, ‘b’ and ‘d’) while EEG signal was recorded. The
F-score method was employed to assess the discriminative power for each
feature, and a subgroup of features with high discriminative powers was then
selected and fed into classifiers. Five classifiers (i.e., k-Nearest
Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis
(LDA), Random Forest (RF) and AdaBoost (ADA)), which are either pervasive or
advanced in the field of machine learning, were utilized to classify brain
activities in perception of shape-analogous letters. For each classifier, its
parameters and the number of used features were optimized. Based on the
performance comparison among the classifiers, Random Forest (RF) classifier
achieved a maximal accuracy of 74.1%, but it was not statistically
significantly better than the SVM. Our study demonstrated that brain activities
in perception of shape-analogous English letters can be classified based on
EEG signal and showed that random forest classifier outperformed other
classifiers according to the results of comparison.

F41

Computational Analysis of
Smile Weight Distribution across the Face for Accurate Distinction between
Genuine and Posed Smiles
Ahmad Al-dahoud and Hassan UgailIn this paper, we report the results of our recent research
into the understanding of the exact distribution of a smile across the face,
especially the distinction in the weight distribution of a smile between a
genuine and a posed smile. To do this, we have developed a computational
framework for the analysis of the dynamic motion of various parts of the face
during a facial expression, in particular, for the smile expression. The
heart of our dynamic smile analysis framework is the use of optical flow
intensity variation across the face during a smile. This can be utilised to
efficiently map the dynamic motion of individual regions of the face such as
the mouth, cheeks and areas around the eyes. Thus, through our computational
framework, we infer the exact distribution of weights of the smile across the
face. Further, through the utilisation of two publicly available datasets,
namely the CK+ dataset with 83 subjects expressing posed smiles and the MUG
dataset with 35 subjects expressing genuine smiles, we show there is a far
greater activity or weight distribution around the regions of the eyes in the
case of a genuine smile.

F48

Sign Words Annotation
Assistance using Japanese Sign Language Words RecognitionNatsuki Takayama and Hiroki Takahashi
A Japanese sign language corpus is essential to activate analysis and
recognition research of Japanese sign language. It requires collecting large
scale of video data and annotating information to build a sign language
corpus. Generally, building a sign language corpus is tedious work, and
assistance is necessary. This paper describes one of the assistance methods
for annotation tasks of sign words using Japanese sign language words
recognition. The words recognition extracts sign features from a video,
segments it into meaningful units, and annotates word labels to them
automatically. At this time, the user’s annotation tasks can be reduced from
the full-manual work to confirmation and correction of the annotation. The
proposed sign words recognition is composed of body-parts tracking, feature
extraction, and words classification. The five types of approaches including
i) feature fusion and ii) multi- stream HMM to handle the multiple body-parts
are applied and compared. We build a video database of Japanese sign language
words and a manual annotation interface to evaluate the proposed method. The
database includes 92 Japanese sign language words which are signed by ten
native signers. The total number of videos is 4,590, and 3,900 videos of 78
words except for recording and sign errors are used for the evaluation. The
classification accuracies were 75.88% and 93.35% in the signer and trial
opened conditions, respectively, when the parts-based feature fusion and
multi-stream HMM using relative weights for body-parts are employed.
Moreover, the expected work reduction ratio of annotation tasks using the
interface was 38.01%.

F52

REVAM: a Virtual Reality
Application for Inducing Body Size Perception ModificationsCédric Buche and Nathalie Le Bigot
In this paper, we present a 3D virtual environment for inducing body
ownership illusion. The key idea is to take advantage of virtual reality
potential regarding body size perception. The application, called REVAM,
links two main aspects of body size perception: the first one focuses on its
modification and the second one concerns its assessment. Based on some
previous evidence that it is possible to modify body-size perception through
an illusion of ownership over a virtual body, the application proposes to
couple a tactile stimulation when viewing an avatar from a third person
perspective (a condition known to produce this kind of illusion). In
addition, the application offers the possibility to choose between avatars of
different builds, and to perform morphing to reduce the avatars body.
Moreover, the application allows to implicitly measure how people perceive
their body size from an affordance estimation task in which people have to
appreciate if they can pass through doors of different sizes without twisting
their shoulders. To test the application we carried out an experiment on 16
female participants who performed the affordance estimation task five times:
the first time before being exposed to their chosen avatar to get a baseline
measure, and the four other times after being exposed to their avatar in
different situations. These different situations are defined by the crossing
of two experimental factors: morphing (presence or absence) and simultaneous
visuotactile stimulation (presence or absence). A two-way repeated-measures
Anova showed a main effect of the morphing: mean door width through which the
participants estimate they can fit was significantly reduced (p < .05)
when morphing was present. However, this effect did not interact with the
presence of a simultaneous tactile stimulation. This indicates that exposing
people to a virtual body reduced in size, as proposed in the present
application, could be an effective way to modify body size perception, at
least temporarily. The final goal would be to help patients with body image
disorders, such as anorexia nervosa.

F78

Neural Mechanisms of
Social Emotion Perception: An EEG Hyper-scanning StudyLi Zhu, Fabien Lotte, Gaochao Cui, Junhua Li, Changle Zhou and
Andrezj Cichocki
EEG-based hyper-scanning refers to two or more subjects engaged in a task
together or performing the same action together while neurophysiological
signals are simultaneously recorded from them. This is one of the manners for
investigating between-subject neural activities involved in social
interactions. Emotion perception plays an important role in human social
interactions. Interaction and emotional state influence each other. In this
study, we aim to investigate how between-subject interaction modulates
emotion perception based on event related potentials (ERPs), connectivity
analysis and classification analysis. We found that there are distinct
differences appearing between paired subjects who performed the task
together, which are early ERP components (N250 and N400), late ERP components
(P1500 and N1500), and the greater amplitude in N250 for the seconding
responding subject compared to the first one. In the exploration of
connectivity using phase locking value (PLV), we found that there are
significant differences among different frequency bands for each subject
under positive and negative stimuli and the significant difference of
hyper-connectivity existed in the gamma frequency band between positive and
negative stimulus trials. In the classification analysis, we compared the
hyper-features for two individual subjects separately, the performance was
improved when hyper-features of the PLV was employed compared to the features
of power spectrum density.

F82

Investigation on the
Correlation between Eye Movement and Reaction Time under Mental Fatigue
Influence
Vianney Renata, Fan Li, Ching-Hung Lee and
Chun-Hsien Chen
With the recent development of eye tracking technology, research in eye
movement and pattern has increased due to its potential to be a
non-obstructive physiological measure tool. This study attempts to understand
to which extent the eye behavior is relatable with human’s mental chronometry
in responding to changes subjected to different levels of mental
fatigue.
An analysis of the eye movement metrics when interacting with multiple short
performance-based tasks under different states of mental fatigue is
performed. It is concluded that the eye movement has influence in the
resulting reaction time and the mental fatigue state of the individual. Thus,
indicating the relationship as a strong potential to predict an individual’s
mental fatigue state. Another finding is that the relationship between the
eye movement metrics and mental chronometry becomes stronger as the
subjective mental fatigue level increases.

F100

Exactly Periodic Spatial
Filter For SSVEP Based BCIsKiran Kumar G.R. and Ramasubba
Reddy M.
This study introduces a novel, high accuracy, calibration less spatial
filter for reliable steady-state visual evoked potential (SSVEP) extraction
from noisy electroencephalogram (EEG) data. The proposed method, exactly
periodic subspace decomposition (EPSD), utilises the periodic properties of
the SSVEP components to achieve a robust spatial filter for SSVEP extraction.
It tries to extract the SSVEP components by projecting the EEG data onto a
subspace where only the target signal components are retained. The
performance of the method was tested on an SSVEP dataset obtained from ten
subjects and compared with common SSVEP spatial filtering and detection
techniques. The results obtained from the study shows that EPSD consistently
provides a significant improvement in detection performance than other SSVEP
spatial filters used in brain-computer interface (BCI) applications.

F106

Powering Up Attentional
Focus: Validating a school-based deep breathing intervention with mobile
EEG—a pilot explorationKhng Kiat Hui and Ravikiran Mane
Electrophysiological and neuroimaging data are important sources of
information for validating the efficacy or effects of interventions. Many
interventions for children are carried out in the schools especially if they
are educationally relevant. However, factors such as high costs and physical
constraints have typically limited the use of electrophysiological and
neuroimaging tools to laboratory settings. Despite their reduced
capabilities, the appearance of low-cost, quick-to-set-up mobile equipment in
recent years have renewed the possibility of applying such techniques to
monitor effects in school-based interventions. The current study explores the
utility of a low-cost, mobile electroencephalography (EEG) headset system in
detecting neurophysiological effects of a school-based deep breathing
intervention, found in a previous behavioral study to be efficacious in
reducing self-reported state anxiety and enhancing test performance in
children. As part of a larger pilot study, EEG, respiration, and behavioral
data were collected from a group of right-handed 11-year-olds as they
performed a flanker task of attentional focus twice, once with a deep
breathing intervention and once without. Results from power spectral analyses
suggest that the low-cost, low- resolution, mobile EEG system is able to
detect power spectra differences associated with flanker interference and
intervention effects.

Track on Cognitive Human-machine Interaction:
Short Paper

S17

Computational Intelligence
CSA-Based Approach for Machine-Driven Calculation of Outline Curves of
Cutaneous Melanoma
Akemi Gálvez and Andrés IglesiasThis paper addresses the problem of obtaining automatically a
good approximation of the outline curve of skin lesions from dermoscopy
images. This problem appears as a critical step in machine-driven
segmentation of dermoscopy images for semi-automatic early diagnosis of
cutaneous melanoma. Given a set of feature points selected by a specialist,
the method applies a powerful nature-inspired metaheuristic optimization
method called cuckoo search algorithm (CSA) to obtain the free-form
parametric Be ́zier curve that fits the points better in the
least-squares sense. Two illustrative examples of a benign and a malignant
skin lesions (a naevus and a melanoma, respectively) are used to evaluate the
performance of the method. Our experimental results show that the method
performs very well and can be applied as a intermediate step of
semi-automatic image segmentation for early diagnosis of cutaneous melanoma.

S21

EEG-based Evaluation of
Mental Fatigue Using Machine Learning AlgorithmsYisi Liu, Zirui Lan, Han Hua Glenn
Khoo, King Ho Holden Li, Olga Sourina and Wolfgang Mueller-Wittig
When people are exhausted both physically and mentally from overexertion,
they experience fatigue. Fatigue can lead to a decrease in motivation and
vigilance which may result in certain accidents or injuries. It is crucial to
monitor fatigue in workplace for safety reasons and well-being of the
workers. In this paper, Electroencephalogram (EEG)-based evaluation of mental
fatigue is investigated using the state-of-the-art machine learning
algorithms. An experiment lasted around 2 hours and 30 minutes was designed
and carried out to induce four levels of fatigue and collect EEG data from
seven subjects. The results show that for subject-dependent 4-level fatigue
recognition, the best average accuracy of 93.45% was achieved by using 6
statistical features with a linear SVM classifier. With subject-independent
approach, the best average accuracy of 39.80% for 4 levels was achieved by
using fractal dimension, 6 statistical features and a linear discriminant
analysis classifier. The EEG-based fatigue recognition has the potential to
be used in workplace such as cranes to monitor the fatigue of operators who
are often subjected to long working hours with heavy workloads.

S32

Designing a Digital
Fitness Game System for Older Adults in Community Settings
Jinhui Li, Mojisola Erdt, James
Chong Boi Lee, Harsha Vijayakumar, Caroline
Robert and Yin-Leng Theng
Exergames is one of the new innovative approaches used in primary healthcare
programmes. The current study introduces a fitness game system,
HOCAMOSE-VETS, which includes digital rehabilitation exercises and exergames
designed with a particular focus on older adults. Besides the new exergames,
the system also allows care staff to actively schedule, monitor and assess
the progress of older adults’ exercise activities. A survey-based study was
conducted to investigate the overall user acceptance of the digital fitness
game system. We found that users’ perceived ease of use and usefulness have a
significant impact on their actual intention of using the game system.
However, the output quality of the system is not significantly associated
with users’ perception of the usefulness and ease of use of the system. The
findings from this research have provided new insights into designing elderly
fitness games.

S46

Cross Dataset Workload
Classification Using Encoded Wavelet Decomposition FeaturesWei Lun Lim, Olga Sourina and Lipo Wang
For practical applications, it is desirable for a trained classification
system to be independent of task and/or subject. In this study, we show
one-way transfer between two independent EEG workload datasets: from a large
multitasking dataset with 48 subjects to a second Stroop test dataset with 18
subjects. This was achieved with a classification system trained using sparse
encoded representations of the decomposed wavelets in the alpha, beta and
theta power bands, which learnt a feature representation that outperformed
benchmark power spectral density features by 3.5%. We also explore the
possibility of enhancing performance with the utilization of domain
adaptation techniques using transfer component analysis (TCA), obtaining
30.0% classification accuracy for a 4-class cross dataset problem.

S61

A Visual Keyboard System
using Hybrid Dual Frequency SSVEP Based Brain Computer Interface with VOG
IntegrationSaravanakumar
D. and Ramasubba Reddy M.
The focus of this paper is to increase the number of targets and
classification rate in the SSVEP-BCI visual keyboard system. The dual
frequency steady state visual evoked potential (SSVEP) and video-oculography
(VOG) based hybrid system has been developed in this study. The visual
stimuli (targets) were designed using dual frequency SSVEP method. This
method could create more targets through a suitable combination of limited
number of frequencies. The keyboard screen was divided into three sections
(left, middle and right), and each section visual stimuli/keys were designed
with a unique set of frequencies. The webcam based video-oculography was used
to detect the direction of the eye gaze. This selection reduces the issue of
misclassification of SSVEP frequencies. Extended multivariate synchronization
index (EMSI) method is used for SSVEP frequency recognition. Both online and
offline experiments were conducted on 10 subjects and an average online
detection accuracy of 94.987% was obtained with the information transfer rate
(ITR) of 82.786 bits/minutes.

S89

A Novel Visual Keyboard
System for Disabled People/Individuals using Hybrid SSVEP Based Brain
Computer InterfaceSaravanakumar D. and Ramasubba
Reddy M.
This paper aims to design a new stimulus paradigm for SSVEP based keyboard
system. The proposed paradigm was implemented using black and white
checkerboard flickering visual stimuli along with the integration of
video-oculography (VOG). The on-screen speller was designed using three
frequencies. The goal of this study is how to increase more number of targets
using less number of stimulus frequencies. It is achieved by the use of VOG
data. The study was carried out using 36 selected characters. A webcam is
integrated along with the system to obtain VOG data. The webcam captures the
images of the eyes, which in turn is used to detect the eye gaze direction.
This additional information from VOG overcomes the limitations of SSVEP based
spelling system. The extended multivariate synchronization index (EMSI)
method is used for SSVEP frequency recognition. Offline and online analysis
of the experiment were conducted and the duration of recognition of each
character required by the participant was calculated based on the
classification accuracy. Online experiment was conducted on 10 subjects to
validate the accuracy and information transfer rate (ITR) of the system. An
average online detection accuracy of 90.46 % was obtained with the ITR of
65.98 bits/minutes.

S90

Promoting Healthy and
Active Ageing Through Exergames: Effects of Exergames on Senior Adults’
Psychosocial Well-beingChen Li, Jinhui Li, Tan Pham Phat,
Yin-Leng Theng, and Bing Xun Chia
Exercise games (exergames) are defined as the combination of exercise and
interactive fitness games. The previous pilot study has found positive
influences of exergaming on the psychological well-being of senior adults,
and this study aims to further investigate the effects of exergaming playing
on the elderly’s motivation and attitude towards exergames, psychosocial
well-being (sociability and loneliness), and inter- generational perception.
A 2 (pre-test vs. post-test) X 3 (play alone vs. play with elderly vs. play
with youths) mixed quasi-experiment was conducted (N=317) in Singapore. Over
6 weeks’ playing time, the elderly’s attitude towards exergames, their
perception towards youth, and sociability significantly increased. The
elderly’s level of loneliness significantly decreased over 6 weeks,
Exergaming could be considered as a way of promoting healthy and active
ageing.

S95

Prediction of Negative
Symptoms of Schizophrenia from Objective Linguistic, Acoustic and Non-verbal
Conversational Cues. Debsubhra
Chakraborty, Shihao Xu, Zixu Yang, Victoria Chua,
Yasir Tahir, Justin Dauwels, Nadia Magnenat Thalmann, Bhing-Leet Tan and
Jimmy Lee
Speech disorders are among the salient characteristics of negative symptoms
of schizophrenia. Such impairments are often exhibited through disorganized
speech, inappropriate affective prosody, and poverty of speech. The current
method of detecting such symptoms requires the expertise of a trained
clinician, which may be prohibitive due to cost, stigma or high
patient-to-clinician ratio. An objective method to extract non- verbal and
verbal speech-related cues can help to automate and simplify the assessment
method of severity of speech-related symptoms of schizophrenia. In this
paper, a novel automated method is presented which uses speech content from
schizophrenic patients to predict the clinician-assigned subjective ratings
of their negative symptoms. Specifically, the interviews of 50 schizophrenia
patients were recorded and features related to acoustics, linguistics and
non-verbal conversation were extracted. The subjective ratings can be
accurately predicted from the objective features with an accuracy of 64-82%
using machine learning algorithms with leave-one-out cross-validation. Our
findings support the utility of automated speech analysis to aid clinician
diagnosis, monitoring and understanding of schizophrenia.

S99

Personas and Emotional
Design for Public Service Robots: A Case Study with Autonomous Vehicles in
Public TransportationPenny Kong, Henriette Cornet and
Fritz Frenkler
Emerging technologies for future mobility will drastically change the way
humans interact with machines and the environment. The common denominator in
technologies such as autonomous vehicles (AVs) and artificial intelligence is
the absence of the human, which can be addressed with a service robot
designed to appeal to human emotion. As service robots tend to operate in
environments where there is a diversity of users and thus user requirements,
there lies a gap in the definition of how these interactions should be
designed. This paper discusses the use of personas in the development of
service robots for multi-stakeholder environments through a case study on AVs
for public transportation in Singapore.

S105

Predicting Ordinal Level
of Sedation from the Spectrogram of ElectroencephalographyHaoqi Sun, Sunil B. Nagaraj and M. Brandon Westover
In Intensive Care Unit, the sedation level of patients is usually monitored
by periodically assessing the behavioral response to stimuli. However, these
clinical assessments are limited due to the disruption with patients’ sleep
and the noise of observing behaviors instead of the brain activity directly.
Here we train a Gated Recurrent Unit using the spectrogram of
electroencephalography (EEG) based on 166 mechanically ventilated patients to
predict the Richmond Agitation-Sedation Score, scored as ordinal levels of
-5, -4, ... up to 0. The model is able to predict 50% accurate with an error
not larger than 1 level; and 80% accurate with an error not larger than 2
levels on hold-out testing patients. We show typical spectrograms in each
sedation level and interpret the results based on the visualization of the
gradient with respect to the spectrogram. Future improvements include
utilizing the EEG waveforms since waveform patterns are clinically thought to
be associated with sedation levels, as well as training patient-specific
models.

S118

Improved User Interface
for a Virtual Integrated Therapy for Active Living (VITAL) – Health
Box: An Elderly PerspectiveBing Xun Chia, Chuan Cheng, May
Thet Hnin, Zwe Marn Tun Lwin, Tan Phat Pham, Quoc Nam Tran Nguyen and
Yin-Leng Theng
Most technology designs are oriented to the younger population with a lack
of attention from an elderly perspective. This paper aims to gather user
needs and requirements from the elderly and propose a user-friendly interface
for a Virtual Integrated Therapy for Active Living (VITAL) – Health Box
for the elderly population. Utilising usability evaluation method, a focus
group discussion was conducted with 10 participants to gather user needs and
preferences about user interface design for VITAL Health Box. Participants
were asked questions relating to content, preference and needs based on a
discussion guide. A set of improved parameters and user interface was
proposed. Major consideration aspects are UI design and functionalities with
optimisation for elderly people, such as providing clear and simple
instruction information, giving multi-language choices, displaying text and
suitable graphic or icon to reduce the confusion or frustration to the
elderly users. The findings of this research provide insight for designing an
elderly-friendly user interface.

Track on Cybersecurity and Biometrics: Full
Papers

F75

A 3D Approach for the
Visualization of Network Intrusion Detection Data
Wei Zong, Yang-Wai Chow and Willy Susilo
With the increasing threat of cyber attacks, machine learning techniques
have been researched extensively in the area of network intrusion detection.
Such techniques can potentially provide a means for the real-time automated
detection of attacks and abnormal traffic patterns. However,
misclassification is a common problem in machine learning techniques for
intrusion detection, and a lack of insight into why such misclassification
occurs impedes the improvement of machine learning models. This paper
presents an approach to visualizing network intrusion detection data in 3D.
The purpose of this is to facilitate the understanding of network intrusion
detection datasets using a visual representation to reflect the geometric
relationship between various categories of network traffic. This can
potentially provide useful insight to aid the design of machine learning
techniques. This paper demonstrates the usefulness of the proposed 3D
visualization approach by presenting results of experiments on commonly used
network intrusion detection datasets.

F81

Enhancing the Security of
Transformation Based Biometric Template Protection Schemes
Loubna Ghammam, Morgan
Barbier and Christophe Rosenberger
Template protection is a crucial issue in biometrics. Many algorithms have
been proposed in the literature among secure computing approaches,
crypto-biometric algorithm and feature transformation schemes. The BioHashing
algorithm belongs to this last category and has very interesting properties.
Among them, we can cite its genericity since it could be applied on any
biometric modality, the possible cancelability of the generated BioCode and
its efficiency when the secret is not stolen by an impostor. Its main
drawback is its weakness face to a combined attack (zero effort with the
stolen secret scenario). In this paper, we propose a transformation-based
biometric template protection scheme as an improvement of the BioHashing
algorithm where the projection matrix is generated by combining the secret
and the biometric data. Experimental results on two biometric modalities,
namely digital fingerprint and finger knuckle print images, show the benefits
of the proposed method face to attacks while keeping a good efficiency.

F85

Cross-Pocket Gait
RecognitionPatrick
Bours and Thilo Denzer
Gait authentication using a mobile phone’s acceleration sensor offers a
convenient, user-friendly and subtle procedure of authenticating individuals
to their mobile phone. This study analyses the possibility of cross-pocket
gait recognition, which means creating the reference and the probe with the
accelerometer sensor in different trouser pockets (left and right). The
results of our analysis show that there is a significant performance
degradation when comparing same-pocket gait recognition with cross-pocket
gait recognition. In our analysis we have used a new distance metric that
shows to give good (same-pocket) performance results compared to known
analysis methods. We have also shown that a multi-reference template can give
excellent performance without any performance degradation for cross-pocket
gait recognition.

F92

User Dependent Template
Update for Keystroke Dynamics Recognition
Abir Mhenni, Estelle Cherrier, Christophe Rosenberger and Najoua
Essoukri Ben Amara
Regarding the fact that individuals have different interactions with
biometric authentication systems, several techniques have been developed in
the literature to model different users categories. Doddington Zoo is a
concept of categorizing users behaviors into animal groups to reflect their
characteristics with respect to biometric systems. This concept was developed
for different biometric modalities including keystroke dynamics. The present
study extends this biometric classification, by proposing a novel adaptive
strategy based on the Doddinghton Zoo, for the recognition of the user’s
keystroke dynamics. The obtained results demonstrate competitive performances
on significant keystroke dynamics datasets.

Track on Cybersecurity and Biometrics: Short
Papers

S36

A New Black Box Evaluation
Protocol for Biometric Systems
Antoine Cabana, Christophe Charrier and Alain
Louis
As a trending method for the authentication, biometrics tends to be
integrated in various devices, and in particular in smartphones. If the
evaluation is performed on operational device, the biometric sample and
algorithm are not reachable by the assessors. So, these latter have to
perform an evaluation on a system considered as a black box. This kind of
evaluation implies numerous manual comparison.
This paper proposes a methodology to perform an evaluation of biometric
black boxes. Two preliminary experiments were performed in order to determine
an optimized conduct. This paper describes the used methodology to perform
evaluation on black boxes systems, and the results obtained on the systems
under test.

S42

Experiments on Deep Face
Recognition using Partial Faces
Ali Elmahmudi and Hassan UgailFace recognition is a very current subject of great interest
in the area of visual computing. In the past, numerous face recognition and
authentication approaches have been proposed, though the great majority of
them use full frontal faces both for training machine learning algorithms and
for measuring the recognition rates. In this paper, we discuss some novel
experiments to test the performance of machine learning, especially the
performance of deep learning, using partial faces as training and recognition
cues. Thus, this study sharply differs from the common approaches of using
the full face for recognition tasks. In particular, we study the rate of
recognition subject to the various parts of the face such as the eyes, mouth,
nose and the forehead. In this study, we use a convolutional neural network
based architecture along with the pre-trained VGG-Face model to extract
features for training. We then use two classifiers namely the cosine
similarity and the linear support vector machine to test the recognition
rates. We ran our experiments on the Brazilian FEI dataset consisting of 200
subjects. Our results show that the cheek of the face has the lowest
recognition rate with 15% while the (top, bottom and right) half and the 3/4
of the face have near 100% recognition rates.

S66

Kinect vs Lytro in RGB-D
Face RecognitionValeria
Chiesa and Jean-Luc Dugelay
Light field cameras are becoming increasingly popular thanks to higher
capabilities with respect to regular cameras in capturing information of a
scene. Even though the principle associated with structured light sensors is
quite different from the technology behind light field cameras, data provided
by these technologies are similar in terms of depth map. With the aim of
comparing the potential of Kinect and Lytro sensors on face recognition, two
experiments are conducted on separate but publically available datasets and
validated on a database acquired simultaneously with Lytro Illum camera and
Kinect V1 sensor. The results obtained on RGB and depth maps are integrated
with an experiment based on fusion at score level. The introduction of depth
information in the RGB data is found more effective than standard bi
dimensional imaging, especially in case of occlusions.

S73

RHU Keystroke Touchscreen
BenchmarkMohamad
El-Abed, Mostafa Dafer and Christophe Rosenberger
Biometric systems are currently widely used in many applications to control
and verify individual’s identity. Keystroke dynamics modality has been shown
as a promising solution that would be used in many applications such as
e-payment and banking applications. However, such systems suffer from several
performance limitations (such as cross-devices problem) that prevent their
widespread of use in real applications. The objective of this paper is to
provide researchers and developers with a public touchscreen-based benchmark
collected using a mobile phone and a tablet (both portrait and landscape
orientation each). Such a benchmark can be used to assess keystroke-based
matching algorithms. Furthermore, It is mainly developed to measure the
robustness of keystroke matching algorithms vis-a`-vis cross-devices and
orientation variations. An online visualizer for the database is also given
to researchers allowing them to visualize the acquired keystroke signals.

S80

Analysis of Keystroke
Dynamics For the Generation of Synthetic DatasetsDenis Migdal and Christophe
Rosenberger
Biometrics is an emerging technology more and more present in our daily
life. However, building biometric systems requires a large amount of data
that may be difficult to collect. Collecting such sensitive data is also very
time consuming and constrained, s.a. GDPR legislation. In the case of
keystroke dynamics, existing databases have less than 200 users. For these
reasons, we aim at generating a keystroke dynamics synthetic dataset. This
paper presents the generation of keystroke data from known users as a first
step towards the generation of synthetic datasets, and could also be used to
impersonate users’ identity.

S96

A Client based Anomaly
Traffic Detection and Blocking Mechanism by Monitoring DNS Name Resolution
With User Alerting FeatureYong Jin, Kunitaka Kakoi,
Nariyoshi Yamai, Naoya Kitagawa and Masahiko Tomoishi
Malware has become one of the most critical targets of network security
solutions nowadays. Many types of malware receive further instructions from
the C&C servers and the attack targets may be instructed by IP addresses
which causes direct attacks without DNS name resolution from the
malware-infected computers. In the meanwhile, several programs that are
hidden from the users (e.g., malware, virus, etc.) may perform DNS name
resolutions for cyber attacks or other communications. In this paper, we
propose a client based anomaly traffic detection and blocking mechanism by
monitoring DNS name resolution per application program. In the proposed
mechanism, by the collaboration of DNS proxy and packet filter, DNS traffic
is monitored on the client and the traffic destined to the IP addresses
obtained without DNS name resolution or the traffic from unrecognized
programs will be detected and blocked. In addition, in order to mitigate
false positive detection, an alert-window will be shown to let the users
decide whether to allow the traffic or not. We implemented a prototype system
on a Windows 7 client and confirmed that the proposed mechanism worked as
expected.

Track on Cyber Cities and Cyber Manufacturing:
Full Papers

F9

Towards Automatic Optical
Inspection of Soldering DefectsWenting Dai, Abdul Mujeeb, Marius
Erdt and Alexei Sourin
This paper proposes a method for automatic image-based classification of
solder joint defects in the context of Automatic Optical Inspection (AOI) of
Printed Circuit Boards (PCBs). Machine learning-based approaches are
frequently used for image-based inspection. However, a main challenge is to
manually create sufficiently large labeled training databases to allow for
high accuracy of defect detection. Creating such large training databases is
time-consuming, expensive, and often unfeasible in industrial production
settings. In order to address this problem, an active learning framework is
proposed which starts with only a small labeled subset of training data. The
labeled dataset is then enlarged step-by-step by combining K-means clustering
with active user input to provide representative samples for the training of
an SVM classifier. Evaluations on two databases with insufficient and
shifting solder joints samples have shown that the proposed method achieved
high accuracy while requiring only minimal user input. The results also
demonstrated that the proposed method outperforms random and representative
sampling by ~ 3.2% and ~ 2.7%, respectively, and it outperforms the
uncertainty sampling method by ~ 0.5%.

F14

Unsupervised Surface
Defect Detection Using Deep Autoencoders and Data AugmentationAbdul Mujeeb, Wenting Dai, Marius Erdt and Alexei Sourin
Surface level defect detection, such as detecting missing components,
misalignments and physical damages, is an important step in any manufacturing
process. In this paper, similarity matching techniques for manufacturing
defect detection are discussed. We are proposing an algorithm which detects
surface level defects without relying on the availability of defect samples
for training. Furthermore, we are also proposing a method which works when
only one or a few reference images are available. It implements a deep
autoencoder network and trains input reference image(s) along with various
copies automatically generated by data augmentation. The trained network is
then able to generate a descriptor—a unique signature of the reference
image. After training, a test image of the same product is sent to the
trained network to generate a test image descriptor. By matching the
reference and test descriptors, a similarity score is generated which
indicates if a defect is found. Our experiments show that this approach is
more generic than traditional hand-engineered feature extraction methods and
it can be applied to detect multiple type of defects.

F37

An Appearance-Driven
Method for Converting Polygon Soup Building Models for 3D Geospatial
ApplicationsKan
Chen, Henry Johan and Marius Erdt
Polygon soup building models are fine for visualization purposes such as in
games and movies. They, however, are not suitable for 3D geospatial
applications which require geometrical analysis, since they lack connectivity
information and may contain intersections internally between their parts. In
this paper, we propose an appearance-driven method to interactively convert
an input polygon soup building model to a two-manifold mesh, which is more
suitable for 3D geospatial applications. Since a polygon soup model is not
suitable for geometrical analysis, our key idea is to extract and utilize the
visual appearance of the input building model for the conversion. We extract
the silhouettes and use them to identify the features of the building. We then
generate horizontal cross sections based on the locations of the features and
then reconstruct the building by connecting two neighbouring cross sections.
We propose to integrate various rasterization techniques to facilitate the
conversion. Experimental results show the effectiveness of the proposed
method.

Track on Cyber Cities and Cyber Manufacturing:
Short Papers

S54

Securing Spatial Data
Infrastructures in the Context of Smart CitiesKanishk Chaturvedi, Andreas
Matheus, Son H. Nguyen and Thomas H. Kolbe
Spatial Data Infrastructures play a very important role in linking and
integrating various distributed systems in smart city applications. One such
concept called Smart District Data Infrastructure (SDDI) is already being
implemented in different districts of European cities, which allows managing
various actors, stakeholders, sensors, simulation tools and semantic 3D city
models within one common operational framework. Such distributed systems
involve open data sources belonging to different platforms. On the other
side, there are various users and applications who want to access and work on
all these systems in convenient ways using single sign-on. If not secured, it
may cause a major threat to disclose sensitive information to untrusted or unauthorized
entities. This paper presents a novel implementation approach of securing
distributed components of the SDDI in the district Queen Elizabeth Olympic
Park in London. It establishes proper authorization and authentication to
allow privacy, security and controlled access to all stakeholders and the
respective components. The implementation combines the use of
state-of-the-art concepts such as OAuth2 access tokens, OpenID Connect user
claims and Security Assertion Markup Language (SAML) based Single-Sign-On
(SSO) authentication.

S68

Using Mobile Phone Data to
Determine Human Mobility Patterns in Paris
Eric Valega Prawirodidjojo, Rui Jie Quek, Bu–Sung Lee,
Vincent Gauthier and Markus Schläpfer
With the rapid expansion of cities around the world, large number of
movements are made daily as people commute from their homes to their
destinations, including workplaces. From these movements, trends and patterns
can be derived which in turn, can provide valuable insights for urban
planning. This is particularly relevant in the ‘smart cities’ context.
However, such movement data are often difficult to gather and analyse without
infringing on privacy rights, especially with the increasing concerns on
privacy issues. This paper reports on the use of aggregated mobile phone
tracking data together with train network data to analyse movement patterns
in, out, and within La Défense (Paris’ business district). The findings
can assist city planners by providing a better understanding of people’s
travel patterns.

S84

Cloud-Based Dynamic
Streaming and Loading of 3D SceneBudianto Tandianus, Hock Soon
Seah, Tuan Dat Vu and Anh Tú Phan
In this paper, we present an approach for out-of-core dynamic streaming of a
virtual scene. We use the traditional client/server architecture, where the
main responsibility of the client application is visualization and
interaction, and the main responsibility of the server is accepting and
serving requests from the client application. The client will query geometry
in the camera proximity to the server and the server will stream geometry (in
CityGML format) to the client application. We implement the client
application using Unity game engine. Performance comparison between
traditional loading and our dynamic streaming are provided. We also show the
scalability advantage of our work.

Poster Papers

P109

The Role of Wearable
Technology in Children’s CreativityRojin VishkaieIn primary and high school settings across the world, wearable
technologies are becoming more and more common. Augmented reality, is another
increasingly common technology, which when combined with wearables, has the
potential to benefit student learning in different settings. This combination
can potentially be used to support problem-solving and creativity for
children, a relatively unexplored area. In this work, an initial expiration
is described about the contextual variables that can impact the levels of low
to high creativity moments. Our goal is to use wearables and sensors to
further minimize the gap between the topic of children’s creativity by
measuring moments of creative fixation and providing feedback in moments of
low creativity to encourage creativity. The primary contributions of this
work, are a preliminary pilot study with ten primary-school students (K-6), a
presentation of our initial results and finally, an examination and
discussion of how creativity in children can be supported or enhanced by
combining wearable technology and augmented reality.

P110

Deep Learning with Long
Short-term Memory Recurrent Neural Network for Daily Container Volumes of
Storage Yard Predictions in Port. Yinping Gao, Daofang Chang, Chun-Hsien Chen and Ting Fang
With the development of China’s Belt and Road Initiative (BRI), the port
plays a significant role and its operation management faces some pressure. In
this regard, prediction of daily container volumes will provide the manager
with data support for better plan of a storage yard. In this work, by deep
learning the historical dataset, the long short-term memory (LSTM) recurrent
neural network (RNN) is trained and used to predict daily volumes of
containers which will enter the storage yard. The raw dataset of a certain
port from 2013 to 2016 is chosen as the training set and the dataset of 2017
is used as the test set to evaluate the performance of the proposed
prediction model. Then the LSTM model is established with Python and
Tensorflow framework. The structure parameters are adjusted to find the
optimal LSTM network, so as to improve the prediction accuracy. It appears
that the LSTM model with two hidden layers and 30 hidden layer units has less
prediction error between the real data and predicted data of 2017. The
prediction error of daily container volumes between predicted value and real
data of 2017 is about 12.39%, which is less than the people-predicted error.
It is promising that the proposed LSTM RNN model can be applied to predict the
daily volumes of containers and have higher prediction accuracy.

P111

The Behavior Symptoms of
Undergraduates’ Social Anxiety in the Virtual World
Yungang Wei, Lin Huang, Wei Wang, Yanqiu Zhang
and Zihan Wang
Nowadays, the use of virtual world is increasingly popularized, as coding
and analyzing behaviors in the virtual world is more rapid and accurate. The
contemporary undergraduates suffer from severe social anxiety. To cope, the
paper takes the undergraduates' social anxiety as the subject for research
and uses the virtual world to collect the data of specific behavior to
predict the social anxiety in the virtual world, finding that the quantity of
windows, turn frequency and clothing fitness in the virtual world all can
significantly predict the social anxiety yet the measurement of psychological
characteristics such as social anxiety in the virtual world needs further
study.

P112

Effects of Sound Volume
Change When Squeezing a Virtual Soft Object with a Bare HandMie Sato, Zentaro Kimura, Yuki Tanaka, Natsumi Motoura, Naoki
Hashimoto and Arie E. Kaufman
In order to improve the perception of the core part of a virtual soft object
when a user squeezes it with his/her bare hand, we apply multisensory
integration of visual and auditory stimuli to our proposed AR system. The
visual stimuli are real-time stereoscopic images in which the user’s bare
hand is squeezing the virtual soft object in the actual scene. As the
auditory stimuli, we focus on the sound volume change linked to the movements
of the user’s thumb and index finger. The present paper reports the effects
of sound volume change in enhancing the pseudo-softness and the perception of
the core part of a virtual soft object. The experimental results of our study
statistically show that when a user squeezes a virtual soft object with
his/her bare hand, multisensory integration of the visual and auditory
stimuli effectively increases the feeling of grasping and facilitates
handling of the virtual soft object. In addition, auditory stimuli that have
a clearly audible sound volume change at the core part naturally enhance the
perception of the core part of the virtual soft object.

P115

Outliers Removal of Highly
Dense and Unorganized Point Clouds Acquired by Laser Scanners in Urban
Environments
Gerasimos Arvanitis, Aris S. Lalos, Konstantinos Moustakas and Nikos
Fakotakis
Recently, there is a tremendous interest in the processing of unorganized
point clouds, generated using a variety of 3D scanning technologies such as
structured light and LIDAR systems. Without a doubt, the most compelling
problem in this domain is the removal of outliers. To effectively address the
aforementioned issue, we present a novel method, that detects accurately and
efficiently the outliers by exploiting the spatial coherence in the object
geometry and the sparsity of the outliers in the spatial domain. This is
achieved by solving a convenient convex method called Robust PCA (RPCA). To
demonstrate the effectiveness of the proposed technique, we evaluate it by
using real scanned point clouds which are extremely dense consisting of
millions of points.

P116

Real-time Haptic Rendering
of Double-points InteractionXinli Wu, Wenzhen Yang, Minxiong
Zhang, Xin Huang, Xuxiao Wu and Zhigeng Pan
Force generation will improve the immersion and authenticity of the virtual
environment with human computer interaction. Considering the limitations of
single-point interaction with force generation, this paper proposes real-time
force generation methods based on double-points interaction. Analyzing the
double-points interaction behaviors, we propose different equations to
calculate the interactive force under four different interaction states. We
establish an experimental environment to test the efficiency of double-points
haptic interaction. The evaluation results show that the method can generate
interactive force in time, enhance the immersion and authenticity of the
virtual environment, and improve the naturalness of human computer interaction.

P117

Towards Citizen-powered
Cyberworlds for Environmental Monitoring
Maria Krommyda, Evangelos Sdongos, Stefano Tamascelli, Athanasia Tsertou, Geli Latsa and Angelos Amditis
ICT advances in emerging domains such as Internet of Things (IoT), Augmented
Reality/ Virtual Reality (AR/VR), big data analytics, cyber-physical systems
and cloud computing have revolutionized and boosted the creation of
cyberworlds as information spaces that allow us to augment the way we
interact with each other and with the physical world. Naturally, other than
businesses cyberworlds can benefit modern hyper connected societies at their
entirety (transport, mobility, health, smart living, etc.) and further to
that the physical world around us can also be part of such process. In the
present paper the focus is given on citizen-powered cyberworlds for
Environmental Monitoring which are created from crowdsourced observations
engaging, through gamification, citizens and communities. The means of
engagement include serious gaming, collection of geo-tagged IoT, such as
images, video and sensor measurements as well as management and storage of
diverse IoT as OGC compliant observations all conveyed into a dedicated
information space.

P120

Are Online Co-Adaptive
Sensorimotor Rhythm Brain-Computer Interface Training Paradigms Effective?
José Diogo Cunha and Reinhod
SchererOperating
a non-invasive electroencephalogram (EEG) based sensorimotor rhythm
brain-computer interface (BCI) is a skill that typically requires extensive
training. Lately, online co-adaptive feedback training approaches achieved
promising results. Does this also mean that users can have meaningful
BCI-based interactions after training?
To answer this question an online study was conducted with 10 na ̈ıve
(first time) users. The users trained to gain BCI control by playing a
Whack-A-Mole game for about 30 minutes. During this time BCI parameters were
adapting to the users EEG patterns. The adaptation was then stopped and users
continued playing the game with the trained BCI for another 20 minutes. Eight
out of the ten users were able to control the BCI and play the game. These
preliminary results seem to suggest that online co-adaptation is an effective
way to gain BCI control.

P121

Augmented Virtualized
Observation of Hidden Physical Quantities in Occupational Therapy. Alberto Fornaser, Mariolino De Cecco,
Paolo Tomasin, Matteo Zanetti, Giovanni Guandalini, Barbara Gasperini,
Patrizia Ianes, Francesco Pilla and Rossella Ghensi
The human being is used to the management of those physical quantities that
can be sensed by his/her five senses. The technology has grown following the
constraint that tries to provide to the user senses interfaces that result
natural and intuitive. With the born and the spread of virtual worlds, thanks
to the Virtual and Augmented Reality revolution, such paradigm has enlarged
comprising: (i) virtual parameters, (ii) directly sensed parameters, and
(iii) indirectly sensed ones. The spread of sensing, connectivity, and
intelligence in ubiquitous computing can occur using any device, in any
location, and in any format and then fed back to the user has opened the
problem/opportunity of how to synthesize the great amount of indirect and
direct amount of data in a, still, natural way. This paper focuses on the
development of an interface for data fruition by a physician or an
occupational therapist in the context of the remote monitoring of disabled
subjects that try different technological aids in a home scenario. The
objective is the fruition of complex datasets in the most natural and
intuitive way.

P122

Multilingual Semantic
Cyberspace of Scientific Papers Based on WebVR TechnologyMichael Charnine, Konstantin Kuznetsov and Oleg Zolotarev
In this paper we describe a cyberspace of scientific papers in which the
most cited and significant documents are represented by large spheres. The
distance between documents is proportional to their semantic similarity
regardless of language. The measure of semantic similarity of multilingual
documents is proposed. This measure is determined by the maximum correlation
between explicit and implicit connectivity of the documents. The cyberspace
is built entirely automatically from collection of texts using methods:
t-SNE, SCCI and A-Frame. The proposed cyberspace implemented by WebVR and
interactive 3D graphics can be considered as a dynamic learning environment
that is convenient for discovering new significant articles, ideas and
trends. The cyberspace is a powerful tool of information integration and it
allows you to visualize documents of different languages in a single space.

P123

Fatigue Prediction and
Intervention for Continuous Play in Video GamesThanat Damrongwatanapokin and Koji
Mikami
Recently, there are many video games that keep relatively high difficulty
for an extended period of time. However, the game with high level of
challenges will induce more frustration and tiredness causing players to take
more short breaks than usual. While mental fatigue has been studied widely,
there are not many game studies and applications related to mental fatigue.
One of the possible applications that we want to explore is artificial
insertion of an interval serving as a break for players before they get
fatigue and take a break from playing games. We plan to use
Electroencephalogram (EEG) for fatigue monitoring and machine learning
approaches to help predict the right time for intervention. Currently, we
have created a prototype game to be used in the experiment and collect sample
data for the machine learning.